Machine and equipment assets, generally, are engineered to perform particular tasks as part of a business process. For example, assets can include, among other things and without limitation, industrial manufacturing equipment on a production line, drilling equipment for use in mining operations, wind turbines that generate electricity on a wind farm, transportation vehicles, and the like. As another example, assets may include devices that aid in diagnosing patients such as imaging devices (e.g., X-ray or MRI systems), monitoring equipment, and the like. The design and implementation of these assets often takes into account both the physics of the task at hand, as well as the environment in which such assets are configured to operate.
Low-level software and hardware-based controllers have long been used to drive machine and equipment assets. However, the rise of inexpensive cloud computing, increasing sensor capabilities, and decreasing sensor costs, as well as the proliferation of mobile technologies have created opportunities for creating novel industrial and healthcare based assets with improved sensing technology and capable of transmitting data that can then be distributed throughout a network. As a consequence, there are new opportunities to enhance the business value of some assets through the use of novel industrial-focused hardware and software.
Businesses can create competitive advantages by harnessing the power of data and analytics to make better informed decisions. Analytical applications may accelerate the path to insights by integrating big data, advanced analytics, and compelling visualizations in tangible and intuitive software package. Industrial based and healthcare based analytical applications may be stored on the platform as part of a broader Internet of Things (IoT) solution. The analytical applications (also referred to herein as “analytics”) may be created by different vendors using different programming languages. The analytics may have different inputs, different outputs, and behave differently from each other.
Analytics are typically designed to work with data from or about a particular asset or group of assets. For example, an analytical application may analyze data surrounding aircraft based assets, manufacturing based assets, healthcare based assets, energy based assets, transportation based assets, mining assets, and the like, using data from these assets. However, over time, unpredictable factors may cause the performance of an analytic to deteriorate. For example, changes in relationships between input variables of data sets, distributions in the values of the input variables, unexpected changes in the data sets, unexpected changes in a business process, unexpected changes in an asset configuration, and the like, could all affect the performance of an analytic.